Asynchronous transfer mode (ATM) networks provide applications with a QoS connection-oriented service that has guaranteed bandwidth for sending data packets or “traffic” over the network. To send the packets, the application requests a virtual circuit (VC) with an initial bandwidth allocation. After the VC has been assigned to the application, an adaptation layer of the network determines how long to keep the VC open for the initial bandwidth assignment. As long as the sending rate of the packets matches the allocated bandwidth, the VC is kept open, see H. Saran, S. Keshav, “An empirical Evaluation of Virtual Circuit Holding Times in IP over ATM Networks,” Proc. of INFOCOM 1994, Y. Afek, M. Cohen, E. Haalman, Y. Mansour, “Dynamic Bandwidth Allocation Policies, ” 0743-166X/96 IEEE, and S. K. Biswas, R. Izmailov, “Design of a fair Bandwidth allocation Policy for VBR Traffic in ATM Networks,” IEEE/ACM Trans. On Networking, V:8, N:2, April 2000. However, if the application sends packets at a higher or lower rate, then there may be a need to adjust or “renegotiate” the allocated bandwidth.
Bandwidth allocation is a particular problem for streaming content, for example, videos. In videos, frame sizes can vary greatly over both short and long time intervals leading to “burstiness” in the traffic. Scene changes and group of picture (GOP) structures within scenes also impact bandwidth requirements. Any of these conditions can result in either poor utilization or delay. For real-time traffic where delays can not be tolerated, data may be lost.
A number of bandwidth predictors and renegotiation methods are known in the prior art. Typically, those methods are either static (off-line), or dynamic (on-line or real-time). Off-line systems can determine the exact bandwidth characteristics of stored videos. However, off-line systems typically have a complexity and computational overload that are not suited for real-time applications.
Dynamic bandwidth allocation requires a good traffic rate predictor and a decision unit that can determine when to change the service rate and required bandwidth for each outgoing stream while at the same time minimizing the number of updates and the allocated bandwidth. Dynamic bandwidth allocation attempts to maximize utilization by minimizing the amount of allocated bandwidth, while at the same time minimizing buffer occupancies (delay). However, even with dynamic bandwidth allocation schemes, it is still difficult to predict the size of future frames. Frequent real-time scalability in spatial and temporal SNR also increases the complexity of the problem. Quality of Service (QoS) for VBR traffic can be achieved with stringent packet and cell loss rates, delay constraints, and high bandwidth channels, but at the expense of low utilization.
Dynamic bandwidth allocation methods can be split into two groups: synchronous and asynchronous. With synchronous methods, bandwidth allocations are modified periodically, at fixed time intervals. Synchronous bandwidth allocation can suffer from the same problem as off-line methods, because during the fixed time intervals the allocations are static.
With asynchronous methods, bandwidth allocations are updated as needed. For example, periodic renegotiations periodically measure an average arrival rate within a given time interval to determine a new bandwidth for a next interval, see Casilari et al. “Bandwidth renegotiation scheme for VBR video services,” IEEE Electronics Letters, v:35, n:18, September 1999. Grossglauser et al., in “RCBR:A Simple and Efficient Service for Multiple Time Scale Traffic,” IEEE Trans. on Networking, v:5, n:6, December 1997, assume a constant cost per renegotiation and allocated bandwidth. That method is based on bandwidth estimators, and high and low buffer threshold parameters, and a time constant T.
Porikli et al., in “Dynamic Bandwidth Allocation with Optimal Number of Renegotiations in ATM Networks,” Proc. of ICCCN'01, pp. 290-295, 2001, attempt to determine an optimum number of renegotiations, see also U.S. Pat. No. 7,027,403 “Method and System for Minimizing Error in Bandwidth Allocation with an Optimal Number of Renegotiations,” filed by Porikli et al., on May 22, 2001, incorporated herein in its entirety by reference. They measure the current data rate to predict future data rates. They use the current data in a cost function to minimize the cost of renegotiation over time.
In U.S. Pat. No. 7,027,391 “Adaptive Bandwidth Allocation by Wavelet Decomposition and Energy Analysis of Network Traffic,” filed by Sahinoglu on Apr. 26, 2001, incorporated herein by reference in its entirety, measure and group data rates into overlapping vectors during fixed length time intervals. Discrete wavelet transform are applied to the overlapping vector to determine frequency bands and associated energies of the data rate, and allocate bandwidth accordingly.
Adas, in “Using Adaptive Linear Prediction to Support Real-time VBR Video Under RCBR Network Service Model,” IEEE Trans. on Networking, v:6, n:5, October 1998, uses a normalized least-mean-square (NLMS) process to predict a next GOP rate for the purpose of dynamic bandwidth allocation.